14 higher contact rate error

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: epiworldR
 Type: Package
 Title: Fast Agent-Based Epi Models
-Version: 0.0-4
-Date: 2024-02-07
+Version: 0.0-9999
+Date: 2024-04-07
 Authors@R: c(
   person("Derek", "Meyer", role=c("aut","cre"), 
   email="[email protected]", comment = c(ORCID = "0009-0005-1350-6988")),
@@ -21,7 +21,7 @@ URL: https://github.com/UofUEpiBio/epiworldR,
     https://uofuepibio.github.io/epiworldR-workshop/ 
 BugReports: https://github.com/UofUEpiBio/epiworldR/issues
 License: MIT + file LICENSE
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.1
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
 LinkingTo: cpp11
@@ -30,7 +30,8 @@ Suggests:
     rmarkdown,
     tinytest,
     netplot,
-    igraph
+    igraph,
+    data.table
 Imports: 
     utils
 VignetteBuilder: knitr

NEWS.md

@@ -1,4 +1,11 @@
-# epiworldR 0.0-3.9000 (development version)
+# epiworldR 0.0-9999 (development version)
+
+* Force model to update agents' states when running a simulation.
+  This was causing issues when calling `run_multiple()` after a single
+  call of `run()`. Reported on [14](https://github.com/UofUEpiBio/epiworldR/issues/14).
+
+
+# epiworldR 0.0-4
 
 * Added missing checks of tool class when adding a model with `add_too_n`.
 

R/make_saver.R

@@ -164,10 +164,10 @@ run_multiple_get_results <- function(m) {
         i, ", there were no observations."
         )
 
-      class(output[[i]]) <- structure(
-        data.frame(),
-        c("epiworld_multiple_save_i")
-      )
+      class(output[[i]]) <- c(
+        "epiworld_multiple_save_i",
+        class(output[[i]])
+        )
 
     }
 

README.Rmd

@@ -13,12 +13,6 @@ knitr::opts_chunk$set(
 )
 ```
 
-## Versions
-The virtual INSNA Sunbelt 2023 session can be found here: https://github.com/UofUEpiBio/epiworldR-workshop/tree/sunbelt2023-virtual
-
-The in-person INSNA Sunbelt 2023 session can be found here:
-https://github.com/UofUEpiBio/epiworldR-workshop/tree/sunbetl2023-inperson
-
 # epiworldR 
 
 <!-- badges: start -->
@@ -205,7 +199,7 @@ net <- get_transmissions(sir)
 
 # Plotting
 library(epiworldR)
-library(epiworldR)
+library(netplot)
 x <- igraph::graph_from_edgelist(
   as.matrix(net[,2:3]) + 1
   )
@@ -242,6 +236,14 @@ head(ans$reproductive)
 plot(ans$reproductive)
 ```
 
+# Tutorials
+
+- The virtual INSNA Sunbelt 2023 session can be found here: https://github.com/UofUEpiBio/epiworldR-workshop/tree/sunbelt2023-virtual
+
+- The in-person INSNA Sunbelt 2023 session can be found here:
+https://github.com/UofUEpiBio/epiworldR-workshop/tree/sunbetl2023-inperson
+
+
 # Existing Alternatives
 
 Several alternatives to `epiworldR` exist and provide researchers with a range of options, each with its own unique features and strengths, enabling the exploration and analysis of infectious disease dynamics through agent-based modeling. Below is a manually curated table of existing alternatives including ABM [@ABM], abmR [@abmR], cystiSim [@cystiSim], villager [@villager], and RNetLogo [@RNetLogo].

README.md

@@ -1,14 +1,6 @@
 
 <!-- README.md is generated from README.Rmd. Please edit that file -->
 
-## Versions
-
-The virtual INSNA Sunbelt 2023 session can be found here:
-<https://github.com/UofUEpiBio/epiworldR-workshop/tree/sunbelt2023-virtual>
-
-The in-person INSNA Sunbelt 2023 session can be found here:
-<https://github.com/UofUEpiBio/epiworldR-workshop/tree/sunbetl2023-inperson>
-
 # epiworldR
 
 <!-- badges: start -->
@@ -127,11 +119,11 @@ summary(sir)
 #> Number of entities  : 0
 #> Days (duration)     : 50 (of 50)
 #> Number of viruses   : 1
-#> Last run elapsed t  : 456.00ms
-#> Last run speed      : 10.94 million agents x day / second
+#> Last run elapsed t  : 155.00ms
+#> Last run speed      : 32.12 million agents x day / second
 #> Rewiring            : off
 #> 
-#> Global actions:
+#> Global events:
 #>  (none)
 #> 
 #> Virus(es):
@@ -303,16 +295,16 @@ rn <- get_reproductive_number(model_logit)
 # Looking into the agents
 get_agents(model_logit)
 #> Agents from the model "Susceptible-Infected-Removed (SIR) (logit)":
-#> Agent: 0, state: Recovered (2), Has virus: no, NTools: 0, NNeigh: 8
-#> Agent: 1, state: Recovered (2), Has virus: no, NTools: 0, NNeigh: 8
-#> Agent: 2, state: Recovered (2), Has virus: no, NTools: 0, NNeigh: 8
-#> Agent: 3, state: Recovered (2), Has virus: no, NTools: 0, NNeigh: 8
-#> Agent: 4, state: Recovered (2), Has virus: no, NTools: 0, NNeigh: 8
-#> Agent: 5, state: Recovered (2), Has virus: no, NTools: 0, NNeigh: 8
-#> Agent: 6, state: Recovered (2), Has virus: no, NTools: 0, NNeigh: 8
-#> Agent: 7, state: Recovered (2), Has virus: no, NTools: 0, NNeigh: 8
-#> Agent: 8, state: Susceptible (0), Has virus: no, NTools: 0, NNeigh: 8
-#> Agent: 9, state: Recovered (2), Has virus: no, NTools: 0, NNeigh: 8
+#> Agent: 0, state: Recovered (2), Has virus: no, NTools: 0i NNeigh: 8
+#> Agent: 1, state: Recovered (2), Has virus: no, NTools: 0i NNeigh: 8
+#> Agent: 2, state: Recovered (2), Has virus: no, NTools: 0i NNeigh: 8
+#> Agent: 3, state: Recovered (2), Has virus: no, NTools: 0i NNeigh: 8
+#> Agent: 4, state: Recovered (2), Has virus: no, NTools: 0i NNeigh: 8
+#> Agent: 5, state: Recovered (2), Has virus: no, NTools: 0i NNeigh: 8
+#> Agent: 6, state: Recovered (2), Has virus: no, NTools: 0i NNeigh: 8
+#> Agent: 7, state: Recovered (2), Has virus: no, NTools: 0i NNeigh: 8
+#> Agent: 8, state: Susceptible (0), Has virus: no, NTools: 0i NNeigh: 8
+#> Agent: 9, state: Recovered (2), Has virus: no, NTools: 0i NNeigh: 8
 #> ... 99990 more agents ...
 ```
 
@@ -346,7 +338,7 @@ net <- get_transmissions(sir)
 
 # Plotting
 library(epiworldR)
-library(epiworldR)
+library(netplot)
 #> Loading required package: grid
 x <- igraph::graph_from_edgelist(
   as.matrix(net[,2:3]) + 1
@@ -412,6 +404,14 @@ plot(ans$reproductive)
 
 <img src="man/figures/README-unnamed-chunk-6-1.png" width="100%" />
 
+# Tutorials
+
+- The virtual INSNA Sunbelt 2023 session can be found here:
+  <https://github.com/UofUEpiBio/epiworldR-workshop/tree/sunbelt2023-virtual>
+
+- The in-person INSNA Sunbelt 2023 session can be found here:
+  <https://github.com/UofUEpiBio/epiworldR-workshop/tree/sunbetl2023-inperson>
+
 # Existing Alternatives
 
 Several alternatives to `epiworldR` exist and provide researchers with a

inst/include/epiworld/math/lfmcmc/lfmcmc-meat.hpp

@@ -83,6 +83,13 @@ inline LFMCMCProposalFun<TData> make_proposal_norm_reflective(
 
         }
 
+        #ifdef EPI_DEBUG
+        for (auto & p : params_now)
+            if (p < lb || p > ub)
+                throw std::range_error("The parameter is out of bounds.");
+        #endif
+
+
         return;
 
     };

inst/include/epiworld/model-meat.hpp

@@ -2039,21 +2039,19 @@ inline void Model<TSeq>::reset() {
         }
         #endif
 
-    } 
-    else
-    {
-        for (auto & p : population)
-            p.reset();
+    }
 
-        #ifdef EPI_DEBUG
-        for (auto & a: population)
-        {
-            if (a.get_state() != 0u)
-                throw std::logic_error("Model::reset population doesn't match."
-                    "Some agents are not in the baseline state.");
-        }
-        #endif
+    for (auto & p : population)
+        p.reset();
+
+    #ifdef EPI_DEBUG
+    for (auto & a: population)
+    {
+        if (a.get_state() != 0u)
+            throw std::logic_error("Model::reset population doesn't match."
+                "Some agents are not in the baseline state.");
     }
+    #endif
 
     if (entities_backup.size() != 0)
     {

inst/include/epiworld/models/sirlogit.hpp

@@ -3,6 +3,34 @@
 #ifndef EPIWORLD_MODELS_SIRLOGIT_HPP 
 #define EPIWORLD_MODELS_SIRLOGIT_HPP
 
+
+/**
+ * @brief Template for a Susceptible-Infected-Removed (SIR) model
+ * 
+ * @details
+ * In this model, infection and recoveru probabilities are computed
+ * using a logit model. Particularly, the probability of infection
+ * is computed as:
+ * 
+ * \f[
+ * \frac{1}{1 + \exp\left(-\left(\beta_0 E_i + \sum_{i=1}^{n} \beta_i x_i\right)\right)}
+ * \f]
+ * 
+ * where \f$\beta_0\f$ is the exposure coefficient and \f$E_i\f$ is the exposure
+ * number, \f$\beta_i\f$ are the
+ * coefficients for the features \f$x_i\f$ of the agents, and \f$n\f$ is the
+ * number of features. The probability of recovery is computed as:
+ * 
+ * \f[
+ * \frac{1}{1 + \exp\left(-\left(\sum_{i=1}^{n} \beta_i x_i\right)\right)}
+ * \f]
+ * 
+ * where \f$\beta_i\f$ are the coefficients for the features \f$x_i\f$ of the agents,
+ * and \f$n\f$ is the number of features.
+ * 
+ * @param TSeq Type of the sequence (e.g. std::vector, std::deque)
+
+*/
 template<typename TSeq = EPI_DEFAULT_TSEQ>
 class ModelSIRLogit : public epiworld::Model<TSeq>
 {

inst/tinytest/test-multiple.R

@@ -0,0 +1,44 @@
+# result <- suppressWarnings(suppressMessages(invisible({
+
+n <- 5e3
+days <- 50
+nsims <- 50
+
+model_seircon=ModelSEIRCONN(
+  name="covid",
+  n = n,
+  prevalence = 0.001,
+  contact_rate = 20,
+  transmission_rate = 0.5,
+  recovery_rate = 1/7,
+  incubation_days = 3
+  )
+
+run(model_seircon,ndays=days,seed=1912)
+
+saver <- make_saver(
+  "total_hist",
+  "transmission",
+  "transition",
+  "reproductive",
+  "generation"
+)
+
+run_multiple(
+  model_seircon,
+  ndays=days,
+  nsim=nsims,
+  seed=1972,
+  saver=saver,
+  nthreads = 2L
+  )
+
+res1 <- run_multiple_get_results(model_seircon)
+
+res1 <- lapply(res1, data.table::as.data.table)
+
+avg_infected <- res1$total_hist[date==50 & state=="Infected"]$counts |> mean()
+avg_recovered <- res1$total_hist[date==50 & state=="Recovered"]$counts |> mean()
+
+expect_true(abs(avg_infected - 13.8) < 50)
+expect_true(abs(avg_recovered - 4986) < 50)